Libraries I may use called
library(tidyverse)
# install for visualizations
library(ggplot2)
# install to combine date and time
library(lubridate)
# for melting a df
library(reshape)
Reading in the first dataset, perceived health status.
perceived_health_status <- read_csv("../data/perceived_health_status.csv")
Inspecting the data.
perceived_health_status
Filtering.
perceived_health_status_stripped <- perceived_health_status %>%
filter(TIME_PERIOD == 2022) %>%
filter(REF_AREA == "AUT") %>%
filter(Sex == "Total") %>%
filter(Age == "15 years or over")
perceived_health_status_stripped
NA
Explore the time periods the data covers
phs_once <- perceived_health_status %>%
filter(Sex == "Total") %>%
filter(Age == "15 years or over")
phs_once
sort(unique(phs_once$TIME_PERIOD))
[1] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
barplot(table(phs_once$TIME_PERIOD), main = "number of observations of year in the data")

As it appears that after 2007, the number of observations are more
significant in number, I will limit my data to 2007 and later. But,
since it appears the number of observations drops off in 2024, I will
limit my data to a range of 2007-2023.
# input code to limit year range in perceived health status dataset here
education_level <- read_csv("../data/educational_attainment_distribution_age_gender.csv")
education_levels_defined <- read_csv("../data/educational_attainment_distribution.csv")
education_levels_three<- read_csv("../data/educational_attainment.csv")
wellbeing_social<- read_csv("../data/current_wellbeing_exp.csv")
education_level
NA
education_levels_defined
el_third <- education_levels_three %>%
filter(Sex == "Total") %>%
filter(Age == "From 25 to 64 years") %>%
filter(TIME_PERIOD == 2010) %>%
filter(OBS_STATUS == "A") %>%
filter(REF_AREA == "AUT") %>%
filter(STATISTICAL_OPERATION == "OBS")
el_third
NA
el_secondary <- education_levels_defined %>%
filter(Sex == "Total") %>%
filter(Age == "From 25 to 64 years") %>%
filter(TIME_PERIOD == 2010) %>%
filter(OBS_STATUS == "A") %>%
filter(REF_AREA == "AUT")
el_secondary
NA
# sort(unique(el_once$OBS_VALUE))
# sort(unique(el_once$`Educational attainment level`))
# sort(unique(el_secondary$`Educational attainment level`))
# el_once %>%
# group_by(`Educational attainment level`) %>%
# summarize(n = n())
# barplot(table(el_once$`Educational attainment level`), main = "number of observations of that education level in the data")
safety_regions <- read_csv("../data/safety_regions.csv")
safety_regions %>%
filter(TIME_PERIOD == 2010) %>%
filter(REF_AREA == "AUT") #%>%
# filter(Sex == "Total") %>%
# filter(Age == "15 years or over")
wellbeing_social %>%
filter(TIME_PERIOD == 2022) %>%
filter(REF_AREA == "AUT") #%>%
# filter(Unit)
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